The present invention relates generally to the field of process control and more particularly to the integration of metrology and process control.
Quality control for the production of microelectronic devices, such as integrated circuit features formed on semiconductor substrate wafers, often depends upon the accurate measurement of the dimensions of various features formed over the substrate surface. Accurate measurement of the topography of the surface of the substrate facilitates not only a pass/fail inspection routine, but also provides a basis for feedback control of upstream processes and feed forward control of downstream processes.
The desire for precise measurement of the surface topography of a microelectronics device must be tempered by the cost and time involved in obtaining such information. At one extreme, destructive examination of a semi-conductor wafer may reveal precise dimensional information at the expense of a useable wafer and many hours of delay time.
The size of semiconductor devices continues to decrease and the metrology used to measure such devices must respond accordingly. There are many types of both optical and electron based metrology tools available, such as scanning electron microscope (SEM), focused ion beam microscope, focused x-ray microscope and focused optical microscopes including near-field scanning optical microscopy. As the size of devices has decreased, optical imaging metrology for critical dimensions has been abandoned. The scanning electron microscope currently plays a major role for metrology in the semiconductor manufacturing industry. Modem 157 nm lithography technology pushes the limits of top down critical dimension scanning electron microscopes (CDSEM). The technology critical dimension nodes of 120 nm and 100 nm lithography will require more precision and accuracy than the SEM appears to be able to provide while utilizing current technologies.
The critical dimension scanning electron microscope utilizes algorithms based upon the intensity of line scan profiles of images to extract the apparent width of features. While the CDSEM offers quick and repeatable measurements of the intensity profiles of features, it remains difficult to establish the exact morphology of the feature from a top down plan view perspective. This is, in part, due to the extensive electron beam interactions within the specimen. That is, the CDSEM will measure the intensity of the secondary electron signal from pixel to pixel across the sample surface. If a particular feature is flat, e.g. the top of a feature or the bottom of a trench, then the secondary electron signal will be constant as long as the surface material remains constant. When the interaction volume that intersects with the surface area of the material changes, the intensity of the secondary electrons that escape from the surface will also change. This topographic effect results in edge effects that blur the image at locations of morphology change. This blurring produces an uncertainty in the critical dimension measurement. While the shape of the intensity profiles respond to drastic changes in morphology, more subtle changes may be lost within the intensity profiles. When intensity profiles are averaged down a plurality of scan lines, additional feature information may be lost.
A CDSEM is used to develop a top down image as shown in FIGS. 1A and 1B. These images represent an intensity of secondary electrons across the plane of the substrate surface I(x,y). FIG. 1A is an SEM image for a semiconductor wafer I-line metal photoresist having a normal morphology, and FIG. 1B is an SEM image for a similar photoresist having an abnormal morphology. An amplitude modulated waveform P(x) may be constructed as a function of this topographic information by averaging I(x,y) over N number of lines using Equation 1:                               P          ⁡                      (            x            )                          =                                            ∑                              y                =                1                            N                        ⁢                          I              ⁡                              (                                  x                  ,                  y                                )                                              N                                    (        1        )            
FIGS. 2A and 2B illustrate the function P(x) as an amplitude modulated waveform representing the average intensity developed from the normal and abnormal photoresist lines of FIGS. 1A and 1B respectively. Known algorithms may be applied to these waveforms to identify critical dimensions, such as the width of the line at 50% wall height, as identified by dashed lines in FIGS. 2A and 2B. Many processes in the semiconductor industry have relied upon this single parameter characterization of the SEM data. Note, however, that single parameter characterization of the data may fail to discern a difference between normal and abnormal morphologies, for example, when both structures have the same critical dimension of 0.684 um as illustrated in FIGS. 2A and 2B.
Multiple parameter characterization (MPC) refers to the use of functions or groups of measurements where a singular discrete measurement can no longer effectively represent the data. MPC is being developed in many different forms for application to scanning electron microscope data in an attempt to address the shortcomings of single parameter characterization, as described above. The shape and scale of the amplitude modulated waveform P(x) can be described through multiple parameters. FIG. 3 illustrates an amplitude modulated waveform P(x) divided into two portions. The distance between the left and right regions of the waveform in solid lines defines a width measurement W and the distance between the left and right regions of the waveform in dashed lines defines a line space measurement S. At discrete intervals along the height of these regions a measurement may be taken for the width and line space, then plotted as a function of height, as shown in FIG. 4. Curve WN-SN represents the MPC of the normal morphology of FIGS. 1A and 2A. Curve WA-SA represents the MPC of the abnormal morphology of FIGS. 1B and 2B. Here the difference between the normal and abnormal morphologies is readily apparent. Other derived MPC values may be used to illustrate deviations from normal profiles, such as roughness measured as the 3-sigma value of the difference between the maximum and minimum critical dimension values along a line profile. Roughness may be useful for illustrating the abnormal photoresist profile known as scumming, where the resist removal fails to clear the resist between developed photoresist lines.
A database of MPC curves such as shown in FIG. 4 may be established for a particular device/process. Preset process margin templates may be established to define acceptable ranges for the MPC values for the evaluation of subsequently manufactured devices. While this quality control procedure is much improved when compared to single parameter evaluation of inspection data, it suffers from the shortcomings of any database driven system. Such shortcomings include a heavy reliance on numeric processing and a resultant lack of speed and a lack of flexibility that requires the establishment of an entirely new database each time a change is made to the process/device.
Another technique for nondestructively examining microelectronics devices is scanning probe microscopy (SPM). SPM includes various techniques of metrology wherein a probe tip is used to study the surface topography or properties of the surface of a substrate. One such device is the atomic force microscope (AFM). Another such device is the stylus nanoprofilometer (SNP) which has emerged recently as a potential tool for critical dimension metrology for both masks and 157 nm integrated circuit technology. An SNP probe tip makes contact with the substrate with only a small force at only discrete pixel points, thus minimizing the probe wear problems associated with atomic force microscopy, where a probe tip continuously touches, or nearly touches, the surface between points. The SNP probe tip is then retracted from the surface a distance sufficient to clear vertical features, moved horizontally a preset distance, and then moved back toward the surface. The vertical resolution is currently 0.3 nm or less. The distance between pixels determines the scan resolution, which currently may be as small as 1.0 nm. The scan time will be the product of time per pixel and the number of pixels. It is possible to obtain precise and accurate three dimensional topographical information with a stylus nanoprofilometer by collecting multiple adjacent line scans. However, a major disadvantage of the SNP is a lack of speed in collecting topographic information.
Scatterometers are currently being proposed for use in the semiconductor industry to provide high speed two dimensional topographic information. These devices are able to determine a cross-section but are unable to determine how that cross-section varies as a function of distance down a line. While such devices provide the promise of improved metrology for small microelectronics devices, they represent a radical change in the equipment base for the semiconductor manufacturing industry. There is some question whether the benefits achievable with modem scatterometers will justify the capital investment necessary to achieve such benefits.
Thus, three technologies are emerging for in-line metrology for semiconductor manufacturing applications. CDSEM is able to provide 3-dimensional information of x-y verses electron intensity, but electron intensity does not correlate easily to feature height. SPM-based tools are able to provide 3-dimensional information of x-y verses height, but they do so at very slow speeds. Scatterometers are able to provide 2-dimensional information of x or y verses height, but they are unable to determine localized information as the values are statistically determined over a large area.
There is a particular need for a improved process controls for the microelectronics industry. There is a further need for a metrology system that may be used to provide three dimensional (x-y verses height) topographical information with an accuracy sufficient for current and next generation microelectronics devices, with the ability to provide localized information and with a throughput speed sufficient to support in-line feedback and feed forward process control systems.
A method of controlling a process is described herein as including: developing intensity information I(x,y) corresponding to a feature on a surface; developing data corresponding to a three dimensional representation of the feature from the intensity information; developing a multiple parameter characterization of at least one critical dimension of the three dimensional representation; and controlling a process in response to the multiple parameter characterization. The process being controlled may be a downstream process applied to the surface in response to the multiple parameter characterization or a process used to develop a second feature on a second surface in response to the multiple parameter characterization. The method may further include: developing a function P(x) representative of a localized area of the surface as a function of I(x,y); and applying a transform function F(x) to the function P(x) to develop the data corresponding to the three dimensional representation of the feature. The transform function F(x) may be developed as a correlation between the function P(x) and a height vector H(x) representing the surface topography of the localized area or as a ratio of a multiple parameter characterization of the function P(x) and a multiple parameter characterization of the height vector H(x). The function P(x) may be developed by calculating P(x) as a weighted average intensity over the localized area across a plurality of scan lines according to the equation:       P    ⁡          (      x      )        =                    ∑                  l          =          1                N            ⁢                                                  I              ⁡                              (                                  x                  ,                  l                                )                                      *                    [                                                    ∑                                  m                  =                  1                                N                            ⁢                                                (                                      1                                          1                      +                                                                        "LeftBracketingBar"                                                                                    I                              ⁡                                                              (                                                                  x                                  ,                                  l                                                                )                                                                                      -                                                          I                              ⁡                                                              (                                                                  x                                  ,                                  m                                                                )                                                                                                              "RightBracketingBar"                                                A                                                                              )                                3                                      N                    ]                3                            ∑                  l          =          1                N            ⁢                        [                                                    ∑                                  m                  =                  1                                N                            ⁢                                                (                                      1                                          1                      +                                                                        "LeftBracketingBar"                                                                                    I                              ⁡                                                              (                                                                  x                                  ,                                  l                                                                )                                                                                      -                                                          I                              ⁡                                                              (                                                                  x                                  ,                                  m                                                                )                                                                                                              "RightBracketingBar"                                                A                                                                              )                                3                                      N                    ]                3            
where P(x) is the reduced amplitude modulated waveform, I(x) is the intensity matrix, N is the number of lines used to calculate the localized waveform, and A is one-half of the total range of the data set. The method may be applied when the feature is a photoresist feature on a first semiconductor substrate and wherein the process controlled is a lithography process.
In one embodiment a method of controlling microelectronic device manufacturing is described as including: developing a photoresist feature on a semiconductor substrate; using a scanning electron microscope to develop secondary electron signal intensity information I(x,y) corresponding to the photoresist feature; developing data corresponding to a three dimensional representation of the photoresist feature from the secondary electron signal intensity information; and developing a multiple parameter characterization of at least one critical dimension of the three dimensional representation. The method may further include controlling an etch process for the semiconductor wafer in response to the multiple parameter characterization.
In a further embodiment, a method of controlling a process is described as: using a scanning electron microscope to develop secondary electron signal intensity information corresponding to a final feature resulting from an etching process; developing data corresponding to a three dimensional representation of the final feature from the secondary electron signal intensity information corresponding to the final feature; developing a multiple parameter characterization of at least one critical dimension of the three dimensional representation of the final feature; and comparing the multiple parameter characterization of the final feature to device performance data.
A method of controlling a manufacturing process is described herein as including: developing intensity information I(x,y) corresponding to a feature on a surface; developing data corresponding to a three dimensional representation of the feature from the intensity information; developing a multiple parameter characterization of at least one critical dimension of the three dimensional representation; and comparing the multiple parameter characterization to a predetermined criteria for evaluating acceptability of the feature. When applied to a semiconductor device manufacturing process, the method may further include: characterizing scale information for the feature as a graph of the critical dimension versus height of the feature; identifying the predetermined criteria as an area on the graph; and evaluating acceptability of the feature by determining if the critical dimension versus height of the feature falls within the area on the graph.
An apparatus for controlling a process is described herein as including: a means for producing intensity data IN(x,y) corresponding to a feature on a surface; a means for producing data corresponding to a three dimensional representation of the feature as a function of the intensity data IN(x,y); a means for developing a multiple parameter characterization of at least one critical dimension of the three dimensional representation; and a processing apparatus responsive to the multiple parameter characterization. The processing apparatus may be responsive to the multiple parameter characterization to produce a second feature on a second surface or to further process the feature on the surface.